Self-supervised Contrastive Learning
has attracted much attention for pre-training. The widely adopted contrastive learning [1]}, [2]} optimizes the model by instance discrimination. Specifically, it learns to embed the features of differently augmented versions of the same image to be similar, while to be dissimilar if they came from different images.
Some recent works [3]} have leveraged the idea of MixUp for contrastive learning.
Our method is different from these methods in: (1) The above methods regard the mixed samples as queries and the original samples as keys, while we additionally consider a reverse operation to exchange their roles.
(2) In our method, the negative samples come from the same video as the positive samples, they can be viewed as hard negative samples, which is important in contarstive learning [4]}.
